Path Optimization along Buoys Based on the Shortest Path Tree with Uncertain Atmospheric and Oceanographic Data

Area of Science:

• Maritime Navigation
• Computational Oceanography
• Logistics Optimization

Background:

• Designing optimal ship routes is crucial for economic benefits, safety, and timely arrivals.
• Existing methods often struggle to dynamically adapt to changing weather conditions, leading to inefficiencies.
• Accurate prediction of ship speed considering hydrometeorological factors is essential for effective route planning.

Purpose of the Study:

• To develop and validate an optimized ship routing algorithm that accounts for adverse weather conditions.
• To improve navigation efficiency by minimizing detours and travel time.
• To enhance safety and fuel economy through intelligent route selection.

Main Methods:

• The study models navigation routes using a tree structure with navigation marks as nodes.
• Path cost is calculated by dividing distance by ship speed, adjusted using Aertssen's deceleration formula based on real-time hydrometeorological data.
• An enhanced Dijkstra algorithm is employed to find the shortest path considering these dynamic costs.

Main Results:

• Route 1, though longer (34,717m), was faster (1.53h) under specific adverse weather (5.877m/s wind, 0.860m/s wave) compared to Route 2 (30,223m, 1.55h).
• The algorithm demonstrated that a longer route can be more time-efficient when accounting for weather impacts on ship speed.
• Calculated ship speeds were 12.243 km/h for Route 1 and 10.523 km/h for Route 2.

Conclusions:

• The proposed navigation algorithm effectively optimizes ship routes by integrating uncertain weather data.
• The shortest path tree approach, combined with speed adjustments, proves effective in reducing navigation time.
• This method offers a viable solution for enhancing maritime safety, fuel efficiency, and punctuality.